Campaign 2014 (PhD Chapter 1)

This series of files compile all analyses done during Chapter 1 for the local campaign of 2014:

All analyses have been done with PRIMER-e 6 and R 3.6.0.

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Caracteristics of each campaign

2014 2016 2017
Sampling date August-September June to August July
Criteria for perturbation Potentially impacted if close to the city or industries, References outside the bay Human-impacted if in a region with a highly populated area, industries and maritime activities, Reference if none of these criteria Human-impacted if in a region with a highly populated area, industries and maritime activities, Reference if none of these criteria
Regions considered BSI BSI, CPC, BDA, MR BSI, MR
Number of sampled stations 40 (20 HI, 20 R) 78 (26 BSI, 19 CPC, 18 BDA, 15 MR) 126 (111 BSI, 15 MR)
Parameters sampled Organic matter yes yes yes
Photosynthetic pigments no yes yes
Sediment grain-size yes yes yes
Heavy-metals yes yes (for a limited number of stations) no (interpolated based on 2014 and 2016 values)
Benthic communities Compartment targeted Macro-infauna Macro-infauna Macro-infauna
Sieved used 500 µm 1 mm 500 µm and 1 mm
Conservation technique Formaldehyle Formaldehyle Formaldehyle
Others N.A. N.A. N.A.

We used data from subtidal ecosystems (see metadata files for more information). Only stations that have been sampled both for abiotic parameters and benthic species were included.

Selected variables for the analyses:

Abundances of Bipalponephtys neotena (Bneo) and Spisula solidissima (Ssol) were also considered (see IndVal and SIMPER results).

Statistics for each variable considered:

  Mean SD SE Median Min Max 95% CI
depth 6.970 1.611 0.255 7.250 4.000 9.600 0.499
om 1.368 1.465 0.232 0.868 0.187 8.260 0.454
gravel 0.017 0.076 0.012 0.000 0.000 0.481 0.024
sand 0.148 0.358 0.057 0.000 0.000 1.000 0.111
silt 0.004 0.006 0.001 0.001 0.000 0.022 0.002
clay 0.830 0.361 0.057 0.992 0.000 1.000 0.112
arsenic 2.720 1.259 0.199 2.250 1.100 6.000 0.390
cadmium 0.116 0.045 0.007 0.110 0.030 0.220 0.014
chromium 65.520 29.623 4.684 63.200 10.900 143.300 9.180
copper 11.045 8.675 1.372 7.300 2.200 32.400 2.688
iron 64222.926 31677.444 5008.644 60284.230 14089.920 188857.220 9816.761
manganese 1412.044 1050.987 166.176 1106.625 251.670 5962.190 325.698
mercury 0.014 0.043 0.007 0.000 0.000 0.250 0.013
lead 4.308 2.945 0.466 3.110 1.020 12.180 0.913
zinc 53.163 23.870 3.774 45.150 15.900 101.500 7.397
S 20.475 7.906 1.250 18.500 6.000 35.000 2.450
N 640.975 703.306 111.202 176.500 14.000 2103.000 217.953
H 1.840 0.410 0.065 1.917 0.911 2.737 0.127
J 0.636 0.155 0.025 0.622 0.315 0.938 0.048

1. Data manipulation

For the following analyses, independant variables are habitat parameters and heavy metal concentrations, dependant variables are diversity indices.

1.1. Identification of outliers

To identify stations that are not consistent with the others, we used the multivariate Cook’s Distance (CD) on the uncorrelated variables. A significative threshold of 4 times the mean of CD has been established.

Based on Cook’s Distance, we identified stations 1 and 29 as general outliers. They have been deleted for the following analyses.

1.2. Correlations between parameters

Correlations have been calculated with Spearman’s rank coefficient.

Correlation coefficients between habitat parameters and metals concentrations
  om gravel sand silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc
om 1 -0.562 -0.118 -0.361 0.545 0.565 0.324 0.206 0.786 -0.123 0.363 0.701 0.656 0.676
gravel -0.562 1 0.243 0.344 -0.752 -0.417 -0.211 -0.134 -0.498 -0.046 -0.379 -0.516 -0.506 -0.544
sand -0.118 0.243 1 -0.616 -0.66 -0.327 -0.478 -0.554 -0.405 -0.543 -0.506 -0.287 -0.418 -0.464
silt -0.361 0.344 -0.616 1 -0.138 -0.13 0.284 0.394 -0.111 0.328 0.077 -0.19 -0.045 -0.018
clay 0.545 -0.752 -0.66 -0.138 1 0.577 0.406 0.381 0.629 0.368 0.598 0.606 0.638 0.663
arsenic 0.565 -0.417 -0.327 -0.13 0.577 1 0.466 0.403 0.672 0.279 0.571 0.581 0.654 0.589
cadmium 0.324 -0.211 -0.478 0.284 0.406 0.466 1 0.865 0.528 0.6 0.796 0.462 0.808 0.792
chromium 0.206 -0.134 -0.554 0.394 0.381 0.403 0.865 1 0.463 0.766 0.798 0.456 0.761 0.739
copper 0.786 -0.498 -0.405 -0.111 0.629 0.672 0.528 0.463 1 0.234 0.577 0.648 0.725 0.832
iron -0.123 -0.046 -0.543 0.328 0.368 0.279 0.6 0.766 0.234 1 0.68 0.136 0.459 0.446
manganese 0.363 -0.379 -0.506 0.077 0.598 0.571 0.796 0.798 0.577 0.68 1 0.591 0.798 0.757
mercury 0.701 -0.516 -0.287 -0.19 0.606 0.581 0.462 0.456 0.648 0.136 0.591 1 0.726 0.661
lead 0.656 -0.506 -0.418 -0.045 0.638 0.654 0.808 0.761 0.725 0.459 0.798 0.726 1 0.921
zinc 0.676 -0.544 -0.464 -0.018 0.663 0.589 0.792 0.739 0.832 0.446 0.757 0.661 0.921 1

According to these results, the following variables are highly correlated (\(|\rho|\) > 0.80) so they have been considered together in the regressions:

  • cadmium, chromium and manganese concentrations (cadmium and manganese deleted)
  • lead and zinc concentrations (zinc deleted)

We also decided to exclude clay content in the regressions, as it tends to increase drasticaly VIFs due to a marginal negative correlation with sand (very high \(R^{2}\)).

2. Permutational Analyses of Variance

Results of univariate PermANOVAs on parameters and multivariate PermANOVA on the whole benthic community are presented in the table below. Variables were normalized and abundances were (log+1) transformed.

Variable Condition Site(Co) Significative groups of similar sites (p > 0.05)
om S S {HI1 HI2 HI3}, {HI4 R2}, {R1 R2 R3}
gravel S {HI1 HI2 HI3 HI4 R3 R4}, {R1 R2}
sand S All sites in the same group
silt S {HI1 HI2 HI3 HI4 R2 R3}, {R1 R2}, {R1 R4}, {R2 R3 R4}
clay S {HI1 HI2 HI3 HI4}, {HI4 R1 R2 R3 R4}, {R1 R2 R3}, {R3 R4}
arsenic S {HI1 HI2}, {HI3 HI4 R2}, {HI3 HI4 R1 R3 R4}
cadmium S All except {HI1 R2}, {HI1 R3}, {HI2 R2}, {HI2 R3}, {HI3 R2}, {HI3 R3}
chromium S {HI1 HI2 HI3 R1 R4}, {HI4 R2 R3 R4}
copper S S {HI1 HI2 HI3}, {HI1 HI3 HI4}, {HI4 R1 R2}, {R1 R2 R3}, {R2 R3 R4}
iron All except {HI1 R3}, {HI2 R3}, {R1 R3}
manganese S {HI1 HI2}, {HI3 HI4 R1 R4}, {R2 R3}
mercury {HI1 HI2 HI3}, {HI2 HI4 R1 R2 R3 R4}
lead S {HI1 HI2}, {HI1 HI3}, {HI4 R1 R2 R3 R4}
zinc S {HI1 HI2 HI3 HI4}, {HI4 R1 R2 R4}, {HI4 R2 R3 R4}
S (500 µm) S {HI1 HI2 HI3}, {HI4 R1 R3 R4}, {HI4 R2 R3 R4}
N (500 µm) S {HI1 HI2 HI3}, {HI4 R2 R3 R4}, {R1 R4}
H (500 µm) All except {HI2 HI3}, {HI3 HI4}
J (500 µm) All except {HI1 HI4}, {HI1 R1}, {HI2 HI3}, {HI2 HI4}, {HI2 R1}, {HI2 R2}
ALL SPECIES (500 µm) S S {HI1 HI2}, {R1 R4}, {R2 R3}

3. Similarity and characteristic species

Let’s have a look at the \(\beta\) diversity within our conditions and sites.

Results of the PERMDISP routine are shown below (mean and SE of the deviation from centroid for each group, i.e. multivariate dispersion). Abundances were (log+1) transformed.

Condition or Site Mean SE
HI 37.2 3.79
R 49.7 1.81
HI1 22.6 2.47
HI2 21.7 0.28
HI3 18.9 2.73
HI4 48.3 3.39
R1 44.9 3.85
R2 40 2.2
R3 41.2 6.52
R4 42.5 4.21

Significative differences in dispersion have been detected between HI and R (p = 0.023), and between {HI1 HI2 HI3} and {HI4 R1 R2 R3 R4} (with the pairwise tests).

Here are the values of the mean Bray-Curtis dissimilarity for each group.

Mean within-group dissimilarity for each condition or site (Bray-Curtis, %)
  HI R P1 P2 P3 P4 R1 R2 R3 R4
Mean BC 0.544 0.72 0.359 0.343 0.302 0.764 0.711 0.631 0.657 0.671

The following analyses allowed to detect species as characteristic of each condition. We used results from PRIMER to justify further their choice.

##                          cluster indicator_value probability
## bipalponephtys_neotena         1          0.9035       0.001
## prionospio_steenstrupi         1          0.8660       0.001
## nephtys_sp                     1          0.8260       0.001
## phoronida                      1          0.7816       0.001
## phyllodoce_groenlandica        1          0.7764       0.001
## capitella_sp                   1          0.7592       0.001
## cirratulidae_spp               1          0.7368       0.001
## limecola_balthica              1          0.7354       0.001
## sarsicytheridea_sp             1          0.6868       0.001
## polychaeta                     1          0.6750       0.001
## scoloplos_armiger              1          0.6743       0.001
## eteone_sp                      1          0.6242       0.001
## hediste_diversicolor           1          0.5500       0.001
## euchone_analis                 1          0.4500       0.001
## pholoe_longa                   1          0.3611       0.032
## pontoporeia_femorata           1          0.3500       0.010
## pholoe_sp                      1          0.3474       0.031
## podocopida                     1          0.3346       0.015
## diastylis_sculpta              1          0.3316       0.014
## glycera_dibranchiata           1          0.3275       0.012
## axinopsida_orbiculata          1          0.3000       0.019
## praxillella_praetermissa       1          0.3000       0.024
## sabellidae_spp                 1          0.3000       0.024
## tharyx_sp                      1          0.3000       0.018
## maldanidae_spp                 1          0.2500       0.042
## spisula_solidissima            2          0.7181       0.001
## echinarachnius_parma           2          0.7000       0.001
## polygordius_sp                 2          0.6005       0.003
## annelida                       2          0.4992       0.003
## cancer_irroratus               2          0.2725       0.046
## 
## Sum of probabilities                 =  98.089 
## 
## Sum of Indicator Values              =  27.96 
## 
## Sum of Significant Indicator Values  =  16.34 
## 
## Number of Significant Indicators     =  30 
## 
## Significant Indicator Distribution
## 
##  1  2 
## 25  5
SIMPER results (mean between-group dissimilarity: 0.858 )
  average sd ratio ava avb cumsum
bipalponephtys_neotena 0.0603 0.0234 2.58 5.11 0.263 0.0703
nephtys_sp 0.0562 0.0274 2.05 4.77 0.139 0.136
prionospio_steenstrupi 0.0441 0.0179 2.46 3.53 0.139 0.187
phoronida 0.0346 0.0203 1.7 2.94 0.0693 0.227
scoloplos_armiger 0.0341 0.0201 1.7 3.02 0.562 0.267
phyllodoce_groenlandica 0.0311 0.0147 2.12 2.68 0.254 0.303
capitella_sp 0.0298 0.0169 1.76 2.58 0.139 0.338
spisula_solidissima 0.0294 0.0257 1.14 0.235 2.06 0.372
phoxocephalus_holbolli 0.0229 0.0228 1 1.08 1.61 0.399
cirratulidae_spp 0.0228 0.0152 1.5 1.94 0.0347 0.426
limecola_balthica 0.0226 0.0159 1.42 1.75 0.0347 0.452
harpacticoida 0.0219 0.0189 1.16 1.94 1.31 0.477
sarsicytheridea_sp 0.0207 0.0156 1.33 1.81 0.0347 0.502
echinarachnius_parma 0.0201 0.0203 0.995 0 1.4 0.525
eteone_sp 0.0161 0.0131 1.23 1.33 0.0549 0.544
pholoe_minuta_tecta 0.0137 0.0176 0.781 0.883 0.302 0.56
polygordius_sp 0.0137 0.0175 0.78 0.245 0.985 0.576
hediste_diversicolor 0.0135 0.0224 0.602 0.861 0 0.592
euchone_analis 0.0126 0.016 0.79 1.14 0 0.606
pholoe_longa 0.0123 0.0134 0.916 0.972 0.239 0.621
pholoe_sp 0.012 0.0138 0.866 0.954 0.145 0.635
oligochaeta 0.0101 0.0258 0.389 0.278 0.343 0.646
mytilus_sp 0.00938 0.0178 0.528 0.135 0.605 0.657
annelida 0.00902 0.0123 0.736 0.0693 0.681 0.668
podocopida 0.00875 0.0137 0.637 0.755 0.0347 0.678
glycera_sp 0.00863 0.0199 0.435 0.352 0 0.688
pseudoleptocuma_minus 0.00852 0.0124 0.686 0.205 0.42 0.698
sabellidae_spp 0.00822 0.0137 0.598 0.727 0 0.707
pontoporeia_femorata 0.00794 0.0119 0.667 0.643 0 0.717
microphthalmus_sczelkowii 0.00775 0.013 0.596 0.609 0.0896 0.726
diastylis_sculpta 0.00738 0.0111 0.668 0.626 0.0347 0.734
spio_filicornis 0.00706 0.0111 0.637 0.355 0.139 0.743
aricidea_sp 0.00699 0.0117 0.6 0.554 0.0896 0.751
tharyx_sp 0.00686 0.0113 0.609 0.534 0 0.759
polychaeta 0.00675 0.00638 1.06 0.624 0.208 0.767
nephtys_caeca 0.00653 0.00968 0.674 0.199 0.283 0.774
glycera_dibranchiata 0.00636 0.00864 0.737 0.504 0.0347 0.782
solenoidea 0.00627 0.00958 0.655 0.425 0.139 0.789
praxillella_praetermissa 0.00618 0.00979 0.631 0.545 0 0.796
axinopsida_orbiculata 0.00615 0.0102 0.6 0.542 0 0.803
bivalvia 0.00597 0.0093 0.642 0.351 0.167 0.81
hemicythere_villosa 0.00579 0.0106 0.547 0.339 0.199 0.817
spiophanes_bombyx 0.00564 0.0122 0.461 0.104 0.219 0.824
halacaridae_spp 0.00549 0.012 0.458 0 0.414 0.83
phyllodoce_sp 0.00509 0.0117 0.435 0.145 0.194 0.836
cancer_irroratus 0.00482 0.00769 0.626 0.0805 0.283 0.841
eucratea_loricata 0.00478 0.00676 0.707 0.243 0.173 0.847
sertulariidae_spp 0.00474 0.00678 0.7 0.555 0.451 0.853
microphthalmus_sp 0.0047 0.00968 0.486 0.42 0 0.858
caprella_septentrionalis 0.00439 0.0148 0.296 0 0.314 0.863
edotia_triloba 0.00423 0.00765 0.553 0.115 0.214 0.868
psammonyx_nobilis 0.00408 0.00939 0.434 0.0693 0.159 0.873
maldanidae_spp 0.00376 0.00688 0.546 0.305 0 0.877
aricidea_acmira_catherinae 0.00337 0.00911 0.37 0.0973 0.145 0.881
cylichna_alba 0.00297 0.00717 0.415 0.271 0 0.885
capitellidae_spp 0.00288 0.00778 0.37 0.19 0.0347 0.888
brachyura 0.00257 0.00623 0.413 0.196 0 0.891
obelia_sp 0.00256 0.00542 0.473 0.0347 0.139 0.894
spionidae_spp 0.00256 0.00506 0.506 0.0549 0.159 0.897
campanulariidae_spp 0.0025 0.0053 0.472 0.658 0.555 0.9

4. Univariate regressions

We used linear models for the all regressions on diversity indices. Outliers and correlated variables were removed from these analyses.

4.1. Simple regressions

These analyses have been done to explore the relationships between variables. As it is a huge number of results to interpret, only multiple regressions will be included in the article.

Adjusted R-squared of simple regressions with all variables
  om gravel sand silt arsenic chromium copper iron mercury lead
S 0.2614 0.07195 -0.02463 0.2452 0.2742 -0.02566 0.3224 -0.009631 0.2168 0.3111
N 0.4527 0.1632 0.03359 0.2031 0.5568 0.1578 0.6478 -0.02778 0.2666 0.7254
H -0.02759 -0.02024 -0.02717 -0.02656 0.01357 0.09775 -0.02313 -0.02743 -0.02778 0.02136
J 0.07927 0.04049 -0.02664 0.05976 0.2216 0.06225 0.1316 -0.01732 0.04197 0.2407
p-values of simple regressions with all variables
  om gravel sand silt arsenic chromium copper iron mercury lead
S 0.0006134 0.05695 0.7414 0.00093 0.0004393 0.7865 0.0001196 0.4264 0.001894 0.0001634
N 2.223e-06 0.006893 0.1393 0.002651 4.565e-08 0.007834 6.818e-10 0.993 0.0005358 7.372e-12
H 0.935 0.6092 0.8847 0.8374 0.2273 0.0315 0.6884 0.9123 0.9951 0.1872
J 0.04813 0.1182 0.843 0.07542 0.00168 0.0712 0.01443 0.5469 0.1142 0.001041

4.2. Multiple regressions

This section presents analyses done (i) to determine which model (metals, parameters or all) describes the best the parameters and (ii) which variables are the most important to explain the parameters.

4.2.1. Best model selection

The aim here is to know which model is the best to explain our data.

Species richness

  n df AIC ∆AIC R2adj
Full model 38 12 254.4 6.963 0.44
Parameters 38 6 256.5 9.101 0.34
Metals 38 8 247.4 0 0.5

Total abundance

  n df AIC ∆AIC R2adj
Full model 38 12 558.6 0 0.78
Parameters 38 6 583.2 24.57 0.54
Metals 38 8 559.1 0.4177 0.77

Shannon index

  n df AIC ∆AIC R2adj
Full model 38 12 50.65 5.511 0.05
Parameters 38 6 51.7 6.562 -0.09
Metals 38 8 45.14 0 0.12

Piélou’s evenness

  n df AIC ∆AIC R2adj
Full model 38 12 -29.29 5.187 0.18
Parameters 38 6 -28.06 6.413 0.05
Metals 38 8 -34.47 0 0.23

4.2.2. Significative variables selection

We identified which variables were selected after an AIC procedure to predict the best the parameters. Results of the variable selection, according to AIC, are shown on the tables below:

  • for the model with all variables
Variable (or combination) S N H J
om +
gravel +
sand/clay - -
silt -
arsenic + - -
chromium/cadmium/manganese - + - -
copper +
iron - + +
mercury +
lead/zinc + +
Adjusted \(R^{2}\) 0.55 0.79 0.17 0.28
  • for the model with habitat parameters
Variable (or combination) S N H J
om + + -
gravel
sand/clay -
silt - -
Adjusted \(R^{2}\) 0.36 0.55 0 0.08
  • for the model with heavy metals
Variable (or combination) S N H J
arsenic -
chromium/cadmium/manganese - - -
copper +
iron + + +
mercury + +
lead/zinc + + -
Adjusted \(R^{2}\) 0.55 0.78 0.14 0.27

Details of the regressions, with diagnostics and cross-validation, are summarized below.

All variables

Species richness
## FULL MODEL
## Adjusted R2 is: 0.44
Fitting linear model: S ~ om + gravel + sand + silt + arsenic + chromium + copper + iron + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 22.43 5.329 4.21 0.0002534 * * *
om -0.6718 1.594 -0.4215 0.6767
gravel 80.67 145.9 0.5529 0.5849
sand -2.728 4.43 -0.6158 0.5432
silt -169.9 279.1 -0.6086 0.5479
arsenic -0.3961 1.915 -0.2068 0.8377
chromium -0.1667 0.08737 -1.908 0.06702
copper -0.0731 0.3337 -0.219 0.8283
iron -5.609e-06 4.196e-05 -0.1337 0.8946
mercury 119.1 101.4 1.175 0.2502
lead 2.754 1.867 1.475 0.1517
Variance Inflation Factors
  om gravel sand silt arsenic chromium copper iron mercury lead
VIF 2.43 1.47 1.66 1.7 2.47 2.69 2.97 1.39 2.05 5.61 
## REDUCED MODEL
## Adjusted R2 is: 0.55
Fitting linear model: S ~ chromium + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 19.76 2.104 9.392 5.673e-11 * * *
chromium -0.1572 0.04005 -3.926 0.0004006 * * *
mercury 89.32 50.72 1.761 0.08726
lead 2.377 0.4508 5.274 7.578e-06 * * *
Variance Inflation Factors
  chromium mercury lead
VIF 1.37 1.13 1.5 
## RMSE for the full model: 9.456865 
## RMSE for the reduced model: 8.355036

Total abundance
## FULL MODEL
## Adjusted R2 is: 0.78
Fitting linear model: N ~ om + gravel + sand + silt + arsenic + chromium + copper + iron + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -223.5 291.8 -0.7658 0.4504
om 187.7 87.29 2.151 0.04061 *
gravel -5227 7990 -0.6543 0.5185
sand -33.33 242.6 -0.1374 0.8918
silt -13973 15283 -0.9143 0.3687
arsenic 165.1 104.9 1.575 0.127
chromium 2.42 4.785 0.5057 0.6172
copper 10.37 18.28 0.5676 0.575
iron -0.00279 0.002298 -1.214 0.2352
mercury -4762 5552 -0.8577 0.3986
lead 49.11 102.2 0.4804 0.6348
Variance Inflation Factors
  om gravel sand silt arsenic chromium copper iron mercury lead
VIF 2.43 1.47 1.66 1.7 2.47 2.69 2.97 1.39 2.05 5.61 
## REDUCED MODEL
## Adjusted R2 is: 0.79
Fitting linear model: N ~ om + silt + arsenic + chromium + copper + iron
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -302.5 170.5 -1.775 0.0858
om 145.2 45.39 3.2 0.003167 * *
silt -21342 10687 -1.997 0.05468
arsenic 203.7 64.78 3.145 0.00365 * *
chromium 4.306 2.667 1.614 0.1166
copper 17.46 11.1 1.574 0.1257
iron -0.0028 0.002019 -1.386 0.1755
Variance Inflation Factors
  om silt arsenic chromium copper iron
VIF 1.3 1.22 1.56 1.54 1.85 1.25 
## RMSE for the full model: 527.8474 
## RMSE for the reduced model: 485.9128

Shannon index
## FULL MODEL
## Adjusted R2 is: 0.05
Fitting linear model: H ~ om + gravel + sand + silt + arsenic + chromium + copper + iron + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.396 0.3649 6.565 4.846e-07 * * *
om -0.04377 0.1092 -0.4009 0.6916
gravel 9.304 9.993 0.9311 0.36
sand -0.3066 0.3034 -1.011 0.3212
silt 1.546 19.11 0.08088 0.9361
arsenic -0.1636 0.1311 -1.248 0.2228
chromium -0.01339 0.005984 -2.237 0.03371 *
copper 0.004782 0.02286 0.2092 0.8359
iron 4.706e-06 2.874e-06 1.638 0.1131
mercury 3.355 6.943 0.4832 0.6329
lead 0.1006 0.1279 0.7864 0.4385
Variance Inflation Factors
  om gravel sand silt arsenic chromium copper iron mercury lead
VIF 2.43 1.47 1.66 1.7 2.47 2.69 2.97 1.39 2.05 5.61 
## REDUCED MODEL
## Adjusted R2 is: 0.17
Fitting linear model: H ~ gravel + sand + arsenic + chromium + iron + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.37 0.2559 9.262 1.938e-10 * * *
gravel 10.49 7.709 1.36 0.1835
sand -0.3405 0.2133 -1.596 0.1206
arsenic -0.1539 0.1043 -1.476 0.15
chromium -0.01337 0.004087 -3.27 0.002635 * *
iron 4.744e-06 2.559e-06 1.854 0.07331
lead 0.1064 0.06021 1.767 0.08711
Variance Inflation Factors
  gravel sand arsenic chromium iron lead
VIF 1.21 1.24 2.09 1.96 1.32 2.81 
## RMSE for the full model: 0.543542 
## RMSE for the reduced model: 0.4367579

Piélou’s evenness
## FULL MODEL
## Adjusted R2 is: 0.18
Fitting linear model: J ~ om + gravel + sand + silt + arsenic + chromium + copper + iron + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.836 0.1275 6.558 4.938e-07 * * *
om -0.02041 0.03813 -0.5352 0.5969
gravel 2.635 3.491 0.755 0.4568
sand -0.1037 0.106 -0.9784 0.3366
silt 2.115 6.677 0.3168 0.7538
arsenic -0.06142 0.04581 -1.341 0.1912
chromium -0.002611 0.00209 -1.249 0.2223
copper 0.0009197 0.007985 0.1152 0.9092
iron 1.421e-06 1.004e-06 1.415 0.1684
mercury 0.5915 2.425 0.2439 0.8092
lead 0.0121 0.04466 0.2708 0.7886
Variance Inflation Factors
  om gravel sand silt arsenic chromium copper iron mercury lead
VIF 2.43 1.47 1.66 1.7 2.47 2.69 2.97 1.39 2.05 5.61 
## REDUCED MODEL
## Adjusted R2 is: 0.28
Fitting linear model: J ~ sand + arsenic + chromium + iron
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.8387 0.08058 10.41 5.899e-12 * * *
sand -0.09731 0.07284 -1.336 0.1907
arsenic -0.05494 0.01978 -2.778 0.008952 * *
chromium -0.001878 0.001015 -1.851 0.0732
iron 1.324e-06 8.344e-07 1.587 0.1221
Variance Inflation Factors
  sand arsenic chromium iron
VIF 1.21 1.14 1.4 1.23 
## RMSE for the full model: 0.1558295 
## RMSE for the reduced model: 0.1458733

Parameters

Species richness
## FULL MODEL
## Adjusted R2 is: 0.34
Fitting linear model: S ~ om + gravel + sand + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 20.97 2.352 8.917 2.629e-10 * * *
om 1.826 0.8264 2.209 0.03421 *
gravel 18.25 133.8 0.1363 0.8924
sand -3.246 3.644 -0.8906 0.3796
silt -595.2 248.5 -2.395 0.02246 *
Variance Inflation Factors
  om gravel sand silt
VIF 1.16 1.24 1.25 1.39 
## REDUCED MODEL
## Adjusted R2 is: 0.36
Fitting linear model: S ~ om + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 19.72 1.89 10.44 2.739e-12 * * *
om 2.091 0.7595 2.753 0.009287 * *
silt -490.2 189.9 -2.582 0.01418 *
Variance Inflation Factors
  om silt
VIF 1.08 1.08 
## RMSE for the full model: 7.353781 
## RMSE for the reduced model: 7.107901

Total abundance
## FULL MODEL
## Adjusted R2 is: 0.54
Fitting linear model: N ~ om + gravel + sand + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 613.8 173 3.548 0.001188 * *
om 227.1 60.79 3.736 0.0007077 * * *
gravel -4810 9846 -0.4885 0.6285
sand -494.4 268.1 -1.844 0.07419
silt -40859 18282 -2.235 0.03231 *
Variance Inflation Factors
  om gravel sand silt
VIF 1.16 1.24 1.25 1.39 
## REDUCED MODEL
## Adjusted R2 is: 0.55
Fitting linear model: N ~ om + sand + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 615.5 171 3.599 0.001004 * *
om 228 60.08 3.795 0.0005805 * * *
sand -549 240.9 -2.279 0.02906 *
silt -45299 15684 -2.888 0.006695 * *
Variance Inflation Factors
  om sand silt
VIF 1.16 1.14 1.21 
## RMSE for the full model: 645.8812 
## RMSE for the reduced model: 647.9296

Shannon index
## FULL MODEL
## Adjusted R2 is: -0.09
Fitting linear model: H ~ om + gravel + sand + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.903 0.1588 11.99 1.435e-13 * * *
om -0.01335 0.05579 -0.2393 0.8123
gravel 7.471 9.037 0.8268 0.4143
sand -0.1523 0.2461 -0.6188 0.5403
silt -12.1 16.78 -0.7212 0.4759
Variance Inflation Factors
  om gravel sand silt
VIF 1.16 1.24 1.25 1.39 
## REDUCED MODEL
## Adjusted R2 is: 0
Fitting linear model: H ~ 1
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.846 0.06792 27.19 4.715e-26 * * *

Quitting from lines 448-452 (C1_analyses_14B.Rmd) Error in Qr$qr[p1, p1, drop = FALSE] : indice hors limites De plus : There were 26 warnings (use warnings() to see them)

## RMSE for the full model: 0.5070976 
## RMSE for the reduced model: 0.4209683

Piélou’s evenness
## FULL MODEL
## Adjusted R2 is: 0.05
Fitting linear model: J ~ om + gravel + sand + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.6552 0.05559 11.79 2.272e-13 * * *
om -0.0262 0.01953 -1.341 0.189
gravel 2.436 3.164 0.7698 0.4469
sand -0.0341 0.08615 -0.3958 0.6948
silt 2.773 5.875 0.472 0.64
Variance Inflation Factors
  om gravel sand silt
VIF 1.16 1.24 1.25 1.39 
## REDUCED MODEL
## Adjusted R2 is: 0.08
Fitting linear model: J ~ om
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.6863 0.03329 20.61 1.608e-21 * * *
om -0.03401 0.01662 -2.046 0.04813 *
Variance Inflation Factors
  om
VIF 1 
## RMSE for the full model: 0.1641308 
## RMSE for the reduced model: 0.1532292

Metals

Species richness
## FULL MODEL
## Adjusted R2 is: 0.5
Fitting linear model: S ~ arsenic + chromium + copper + iron + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 19.75 3.205 6.163 7.728e-07 * * *
arsenic -0.05094 1.529 -0.03331 0.9736
chromium -0.1587 0.05556 -2.857 0.007575 * *
copper 0.01414 0.247 0.05725 0.9547
iron 2.307e-06 3.58e-05 0.06443 0.949
mercury 89.09 53.99 1.65 0.109
lead 2.364 1.184 1.996 0.05477
Variance Inflation Factors
  arsenic chromium copper iron mercury lead
VIF 2.08 1.81 2.32 1.25 1.15 3.76 
## REDUCED MODEL
## Adjusted R2 is: 0.55
Fitting linear model: S ~ chromium + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 19.76 2.104 9.392 5.673e-11 * * *
chromium -0.1572 0.04005 -3.926 0.0004006 * * *
mercury 89.32 50.72 1.761 0.08726
lead 2.377 0.4508 5.274 7.578e-06 * * *
Variance Inflation Factors
  chromium mercury lead
VIF 1.37 1.13 1.5 
## RMSE for the full model: 9.115075 
## RMSE for the reduced model: 8.355036

Total abundance
## FULL MODEL
## Adjusted R2 is: 0.77
Fitting linear model: N ~ arsenic + chromium + copper + iron + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.242 193.4 0.006422 0.9949
arsenic 35.05 92.29 0.3797 0.7067
chromium -3.495 3.353 -1.042 0.3053
copper 7.377 14.91 0.4949 0.6242
iron -0.002257 0.00216 -1.045 0.3042
mercury 5395 3258 1.656 0.1078
lead 185.2 71.47 2.591 0.01446 *
Variance Inflation Factors
  arsenic chromium copper iron mercury lead
VIF 2.08 1.81 2.32 1.25 1.15 3.76 
## REDUCED MODEL
## Adjusted R2 is: 0.78
Fitting linear model: N ~ chromium + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.314 129.8 0.04865 0.9615
chromium -5.973 2.47 -2.418 0.02111 *
mercury 5506 3128 1.76 0.08738
lead 228.9 27.8 8.235 1.312e-09 * * *
Variance Inflation Factors
  chromium mercury lead
VIF 1.37 1.13 1.5 
## RMSE for the full model: 508.8073 
## RMSE for the reduced model: 345.0003

Shannon index
## FULL MODEL
## Adjusted R2 is: 0.12
Fitting linear model: H ~ arsenic + chromium + copper + iron + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.121 0.2237 9.481 1.126e-10 * * *
arsenic -0.08651 0.1068 -0.8102 0.424
chromium -0.008311 0.003879 -2.143 0.0401 *
copper 0.02171 0.01724 1.259 0.2174
iron 4.808e-06 2.499e-06 1.924 0.0636
mercury 1.564 3.769 0.4149 0.681
lead -0.0153 0.08268 -0.1851 0.8544
Variance Inflation Factors
  arsenic chromium copper iron mercury lead
VIF 2.08 1.81 2.32 1.25 1.15 3.76 
## REDUCED MODEL
## Adjusted R2 is: 0.14
Fitting linear model: H ~ chromium + iron
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.063 0.1643 12.56 1.608e-14 * * *
chromium -0.007078 0.002524 -2.804 0.008169 * *
iron 3.82e-06 2.35e-06 1.626 0.113
Variance Inflation Factors
  chromium iron
VIF 1.19 1.19 
## RMSE for the full model: 0.4801949 
## RMSE for the reduced model: 0.4073799

Piélou’s evenness
## FULL MODEL
## Adjusted R2 is: 0.23
Fitting linear model: J ~ arsenic + chromium + copper + iron + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.7319 0.07848 9.326 1.652e-10 * * *
arsenic -0.03071 0.03746 -0.8198 0.4186
chromium -0.0006143 0.001361 -0.4515 0.6548
copper 0.006537 0.00605 1.081 0.2882
iron 1.462e-06 8.767e-07 1.668 0.1054
mercury -0.3067 1.322 -0.232 0.8181
lead -0.03163 0.029 -1.09 0.2839
Variance Inflation Factors
  arsenic chromium copper iron mercury lead
VIF 2.08 1.81 2.32 1.25 1.15 3.76 
## REDUCED MODEL
## Adjusted R2 is: 0.27
Fitting linear model: J ~ iron + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.6974 0.0542 12.87 7.906e-15 * * *
iron 1.074e-06 6.988e-07 1.536 0.1335
lead -0.02991 0.00771 -3.88 0.0004409 * * *
Variance Inflation Factors
  iron lead
VIF 1.02 1.02 
## RMSE for the full model: 0.1582621 
## RMSE for the reduced model: 0.1391175

5. Multivariate regression

Independant variables are habitat parameters and heavy metal concentrations, dependant variables are species abundances. Outliers and correlated variables have been excluded from the analysis.

This analysis has been done on PRIMER, with a DistLM to identify the variables that explain the most the community variability and with a dbRDA to plot the results.

Elliot Dreujou

2020-01-20